PROJECT SUMMARY Tumor heterogeneity is essential to cancer biology, as the differential survival of treated cell populations is responsible for resistance. Understanding rates of subclonal evolution is therefore vital to developing treatment strategies that can minimize resistance and mortality. However, quantification of intratumoral populations remains a challenge. This is because the number and diversity of samples necessary for accurate quantification, as well as the optimal parameter choices for computational inference algorithms, are unknown. Our lab has shown that current measurement approaches such as single-sample exome-seq are not well- powered to distinguish evolutionary processes within tumors. As a result, even fundamental evolutionary questions, such as the balance of neutral and adaptive evolution in tumors, remain hotly contested. By performing an extensive series of multi-sample, multi-treatment comparative sequencing analyses of triple- negative breast cancer xenografts, we have identified a system of closely-related subclonal populations within a tumor that respond differentially to cisplatin treatment. A unique characteristic of this system of subclones is that they can be treated in a common organoid to determine treatment-dependent evolutionary dynamics of related cancer subpopulations. We propose to leverage this system together with high-throughput sequencing and a powerful high-content confocal imaging technology on engineered organoids to provide verified quantitative computational methods to accurately measure tumor heterogeneity for triple-negative breast cancer and other solid tumors. The project will be led by J. Chuang PhD, an expert in cancer genomics, computational biology, and molecular evolution. The PI collaborates with E. Liu MD and F. Menghi PhD (breast cancer genetics) and O. Anczukow-Camarda PhD (cancer gene expression and organoids), in coordination with the Single Cell Biology core led by P. Robson PhD at The Jackson Laboratory. Aim 1. Credential the quantification of heterogeneity using sequencing and high-content confocal imaging on patient-derived cancer organoid mixtures. Aim 2. Optimize computational approaches for determining heterogeneity from sequencing and spatial data. Aim 3. Determine the prevalence of intratumoral selection in big cancer data. Impact: Results would pave the way for the development of the first evolution-based approaches to cancer treatment, which may dramatically improve morbidity and mortality in metastatic cancers.